Characterizing the Temperature of SAT Formulas

The remarkable advances in SAT solving achieved in the last years have allowed to use this technology to solve many realworld applications, such as planning, formal verification and cryptography, among others. Interestingly, these industrial SAT problems are commonly believed to be easier than class...

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Authors: Almagro Blanco, Pedro, Giraldez Cru, Jesus
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/170872
Online Access:https://hdl.handle.net/11441/170872
https://doi.org/10.1007/s44196-022-00122-4
Access Level:Open access
Keyword:SAT
Hardness. Temperature
Popularity–similarity
Entropy
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spelling Characterizing the Temperature of SAT FormulasAlmagro Blanco, PedroGiraldez Cru, JesusSATHardness. TemperaturePopularity–similarityEntropyThe remarkable advances in SAT solving achieved in the last years have allowed to use this technology to solve many realworld applications, such as planning, formal verification and cryptography, among others. Interestingly, these industrial SAT problems are commonly believed to be easier than classical random SAT formulas, but estimating their actual hardness is still a very challenging question, which in some cases even requires to solve them. In this context, realistic pseudo-industrial random SAT generators have emerged with the aim of reproducing the main features of these application problems to better understand the success of those SAT solving techniques on them. In this work, we present a model to estimate the temperature of real-world SAT instances. This temperature represents the degree of distortion into the expected structure of the formula, from highly structured benchmarks (more similar to real-world SAT instances) to the complete absence of structure (observed in the classical random SAT model). Our solution is based on the popularity–similarity random model for SAT, which has been recently presented to reproduce two crucial features of application SAT benchmarks: scale-free and community structures. This model is able to control the hardness of the generated formula by introducing some randomizations in the expected structure. Using our regression model, we observe that the estimated temperature of the applications benchmarks used in the last SAT Competitions correlates to their hardness in most of the cases.Springer NatureCiencias de la Computación e Inteligencia Artificial2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/170872https://doi.org/10.1007/s44196-022-00122-4reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternational Journal of Computational Intelligence Systems, 15 (1), 69.https://link.springer.com/article/10.1007/s44196-022-00122-4info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1708722026-06-17T12:51:07Z
dc.title.none.fl_str_mv Characterizing the Temperature of SAT Formulas
title Characterizing the Temperature of SAT Formulas
spellingShingle Characterizing the Temperature of SAT Formulas
Almagro Blanco, Pedro
SAT
Hardness. Temperature
Popularity–similarity
Entropy
title_short Characterizing the Temperature of SAT Formulas
title_full Characterizing the Temperature of SAT Formulas
title_fullStr Characterizing the Temperature of SAT Formulas
title_full_unstemmed Characterizing the Temperature of SAT Formulas
title_sort Characterizing the Temperature of SAT Formulas
dc.creator.none.fl_str_mv Almagro Blanco, Pedro
Giraldez Cru, Jesus
author Almagro Blanco, Pedro
author_facet Almagro Blanco, Pedro
Giraldez Cru, Jesus
author_role author
author2 Giraldez Cru, Jesus
author2_role author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
dc.subject.none.fl_str_mv SAT
Hardness. Temperature
Popularity–similarity
Entropy
topic SAT
Hardness. Temperature
Popularity–similarity
Entropy
description The remarkable advances in SAT solving achieved in the last years have allowed to use this technology to solve many realworld applications, such as planning, formal verification and cryptography, among others. Interestingly, these industrial SAT problems are commonly believed to be easier than classical random SAT formulas, but estimating their actual hardness is still a very challenging question, which in some cases even requires to solve them. In this context, realistic pseudo-industrial random SAT generators have emerged with the aim of reproducing the main features of these application problems to better understand the success of those SAT solving techniques on them. In this work, we present a model to estimate the temperature of real-world SAT instances. This temperature represents the degree of distortion into the expected structure of the formula, from highly structured benchmarks (more similar to real-world SAT instances) to the complete absence of structure (observed in the classical random SAT model). Our solution is based on the popularity–similarity random model for SAT, which has been recently presented to reproduce two crucial features of application SAT benchmarks: scale-free and community structures. This model is able to control the hardness of the generated formula by introducing some randomizations in the expected structure. Using our regression model, we observe that the estimated temperature of the applications benchmarks used in the last SAT Competitions correlates to their hardness in most of the cases.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/170872
https://doi.org/10.1007/s44196-022-00122-4
url https://hdl.handle.net/11441/170872
https://doi.org/10.1007/s44196-022-00122-4
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Computational Intelligence Systems, 15 (1), 69.
https://link.springer.com/article/10.1007/s44196-022-00122-4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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